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GPT-5 is the first model from OpenAI that introduces four adjustable levels of thinking, controlling the amount of time and tokens the model uses when responding to a prompt. When selecting which model to use, or whether to use a reasoning model at all, it is important to consider your application’s priorities.
Scenarios like researching and producing a report involve the collection, processing, and generation of large amounts of data. Customers in these scenarios are typically willing to wait many minutes for a high-quality report to be generated. A reasoning model like GPT-5 with medium or high thinking is great for this use case.
Another example is a coding assistant, where you want to vary the amount of thinking based on the complexity of the coding task. Here, you want your customers to have control over the amount of time and level of effort the model exerts before providing a response. GPT-5 or GPT-5 mini with controllable thinking levels are a great solution.
In contrast, a customer service assistant that is answering customer questions live, retrieving information from a highly efficient search index, and providing human-like responses needs to be fast, friendly, and efficient. For these scenarios, OpenAI’s GPT-4.1 is a far better option.
Choosing the right model for your use case can be a challenging endeavor, so we’ve created this simple guide to help you pick between the two latest flagship models from OpenAI – GPT-5 and GPT-4.1.
Azure AI Foundry offers multiple variants of generative AI models to meet diverse customer needs. Two of the most widely used models—GPT-5 and GPT-4.1—serve different purposes depending on your workload, latency sensitivity, and reasoning requirements.
- GPT-5 is optimized for advanced enterprise use cases such as code generation and review, agentic tool calling, and business research. It excels in structured reasoning, multi-step logic, and planning tasks, making it ideal for Copilot-style applications that require deep understanding and orchestration. While it delivers significantly improved accuracy and contextual awareness, it may introduce higher latency due to its reasoning depth and model complexity.
- GPT-4.1 is optimized for high-speed, high-throughput enterprise applications such as real-time chat, customer support, and lightweight summarization. It delivers fast, concise responses with low latency, making it ideal for latency-sensitive workloads and high-volume deployments. While it does not offer the deep reasoning capabilities of GPT-5, GPT-4.1 excels in responsiveness, cost efficiency, and predictable performance across a wide range of general-purpose tasks.
This guide helps you understand the differences and choose the right model for your use case.
GPT-5 vs GPT-4.1 Comparison
| Feature | GPT-5 | GPT-4.1 |
|---|---|---|
| Model Type | Reasoning | Non-reasoning, fast response |
| Best For | Complex reasoning, multi-hop logic, thinking | Real-time chat, short factual queries, high-throughput workloads |
| Latency | Higher (due to deeper reasoning and longer outputs) | Lower (optimized for speed and responsiveness) |
| Throughput | Moderate | High |
| Token Length | 272K tokens in, 128K tokens out (400K total) | 128 K (short context), up to 1M (long-context) |
| Perspective | Structured, analytical, step-by-step | Concise, fast, conversational |
| Cost | Cost | Cost |
| Variants | GPT-5 GPT-5-mini GPT-5-nano |
GPT-4.1 GPT-4.1-mini GPT-4.1-nano |
GPT-5 thinking levels trade-offs
| Reasoning Effort | Description | Depth of Reasoning | Latency | Cost | Accuracy / Reliability | Typical Use Cases |
|---|---|---|---|---|---|---|
| Minimal | Few or no internal reasoning tokens; optimized for throughput and time-to-first-token | Very shallow | Fastest | Lowest | Lowest on complex tasks | Bulk operations, simple transforms |
| Low | Light reasoning with quick judgment | Shallow to light | Fast | Low | Moderate | Triage, short answers, simple edits |
| Medium (Default) | Balanced depth vs. speed; safe general-purpose choice | Moderate | Moderate | Medium | Good for most tasks | Content drafting, moderate coding, RAG Q&A |
| High | Deep, multistep “think-through” for hardest problems | Deep | Slowest | Highest | Highest | Complex planning, analysis, multihop reasoning |
Notes:
- The pattern above applies to GPT-5, GPT-5-mini, and GPT-5-nano; absolute latency and cost scale down with mini and nano but the tradeoffs are the same.
- Parallel tool calls are not supported at Minimal reasoning_effort. If you need parallel tool use, choose Low/Medium/High.
When to use GPT-5
Choose GPT-5 if your application requires:
- Deep, multistep reasoning for hard problems (planning, analysis, complex synthesis and summarization).
- Reliability over raw speed—GPT-5 delivers higher quality and fewer mistakes than prior generations in many tasks, particularly when reasoning is enabled.
- Agentic workflows for Copilot-style tools that must plan, call multiple tools, and act, benefit from GPT-5’s planning ("preamble") and robust tool use.
- Nuanced intent understanding and structured follow-ups: use structured outputs for predictable formats and verbosity to control response length.
Example Use Cases:
- Legal or financial document analysis
- Technical troubleshooting assistants
- Enterprise Copilots with multi-turn logic
- Research summarization and synthesis
When to use GPT-4.1
Choose GPT-4.1 if your application needs:
- Low latency: Ideal for real-time interactions or user-facing chatbots.
- High throughput: Supports large-scale deployments with cost efficiency.
- Long-context handling: Use GPT-4.1 long-context for inputs up to 1M tokens.
- Short, factual responses: Great for Q&A, search, and summarization of short content.
Example Use Cases:
- Customer support chatbots
- Real-time product recommendation engines
- High-volume summarization pipelines
- Lightweight assistants for internal tools
If you're unsure which model to choose, try Model Router in Azure AI Foundry for a ready-to-use solution. Developers can use the model router in Foundry Models to maximize the capabilities of the GPT-5 family models (and other models in Foundry Models) while saving up to 60% on inferencing cost with comparable quality. How to use model router for Azure AI Foundry (preview) – Microsoft Learn
Latency considerations
Understanding the latency differences between GPT-5 and GPT-4.1 is key to selecting the right model for your needs. GPT-5 delivers powerful reasoning and deeper analysis, but this comes with slightly longer wait times before you see your first response, especially for shorter prompts. You may notice that interactions feel slower when accuracy and complex problem-solving are prioritized.
In contrast, GPT-4.1 offers a snappier and more responsive experience, making it ideal for real-time chats, quick Q&A, and high-volume tasks where speed matters most. If your workflow requires instant feedback and low latency, GPT-4.1 is recommended. However, for tasks where advanced reasoning and accuracy are critical—even if responses take a bit longer—GPT-5 is the preferred choice. This trade-off ensures you get the right balance of speed and intelligence for your specific use case.
| Metric | GPT-5 | GPT-4.1 |
|---|---|---|
| TTFT (Time to First Token) | Higher (due to deeper model layers and reasoning) | Lower |
| TBT (Time Between Tokens) | Moderate to high | Low |
| User Perception | May feel slower, especially for short prompts | Feels snappy and responsive |
If you wish to utilize the advanced features of GPT-5 while ensuring consistent latency, we recommend selecting the Provisioned Throughput deployment type. This option provides specific latency service level agreements (SLAs) for latency and is well-suited to use cases where latency sensitivity is critical. Get started with Provisioned Throughput.